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1	Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?
	Parga, Joanna J; Lewin, Sharon; Lewis, Juanita; Montoya-Williams, Diana; Alwan, Abeer; Shaul, Brianna; Han, Carol; Bookheimer, Susan Y; Eyer, Sherry; Dapretto, Mirella; Zeltzer, Lonnie; Dunlap, Lauren; Nookala, Usha; Sun, Daniel; Dang, Bianca H; Anderson, Ariana E
	In: Pediatric research, vol 87, iss 3 (2020)
	Abstract: BackgroundTo characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral statein a cry-translator. To apply thecry-translation algorithm to colic hypothesizing that these cries sound painful.MethodsAssessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translationalgorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.ResultsThe cry-translationalgorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBabycry-translationalgorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic.ConclusionsAcoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. TheChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway.
	Keyword: Abdominal Pain; Acoustics; Automated; Colic; Computer-Assisted; Crying; Female; Humans; Infant; Infant Behavior; Machine Learning; Male; Mobile Applications; Newborn; Paediatrics and Reproductive Medicine; Pain Perception; Pattern Recognition; Pediatrics; Public Health and Health Services; Signal Processing; Sound Spectrography
	URL: https://escholarship.org/uc/item/3h36c0hh
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2	Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?
	Parga, Joanna J.; Lewin, Sharon; Lewis, Juanita. - 2019
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